Get the fitted values from a DFA as a data frame
dfa_fitted(modelfit, conf_level = 0.95, names = NULL)Output from fit_dfa.
Probability level for CI.
Optional vector of names for time series labels. Should be same length as the number of time series.
A data frame with the following columns: ID is an identifier for each time series, time is the time step, y is the observed values standardized to mean 0 and unit variance, estimate is the mean fitted value, lower is the lower CI, and upper is the upper CI.
predicted plot_fitted fit_dfa
# \donttest{
y <- sim_dfa(num_trends = 2, num_years = 20, num_ts = 4)
m <- fit_dfa(y = y$y_sim, num_trends = 2, iter = 50, chains = 1)
#>
#> SAMPLING FOR MODEL 'dfa' NOW (CHAIN 1).
#> Chain 1:
#> Chain 1: Gradient evaluation took 0.000138 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 1.38 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1:
#> Chain 1:
#> Chain 1: WARNING: There aren't enough warmup iterations to fit the
#> Chain 1: three stages of adaptation as currently configured.
#> Chain 1: Reducing each adaptation stage to 15%/75%/10% of
#> Chain 1: the given number of warmup iterations:
#> Chain 1: init_buffer = 3
#> Chain 1: adapt_window = 20
#> Chain 1: term_buffer = 2
#> Chain 1:
#> Chain 1: Iteration: 1 / 50 [ 2%] (Warmup)
#> Chain 1: Iteration: 5 / 50 [ 10%] (Warmup)
#> Chain 1: Iteration: 10 / 50 [ 20%] (Warmup)
#> Chain 1: Iteration: 15 / 50 [ 30%] (Warmup)
#> Chain 1: Iteration: 20 / 50 [ 40%] (Warmup)
#> Chain 1: Iteration: 25 / 50 [ 50%] (Warmup)
#> Chain 1: Iteration: 26 / 50 [ 52%] (Sampling)
#> Chain 1: Iteration: 30 / 50 [ 60%] (Sampling)
#> Chain 1: Iteration: 35 / 50 [ 70%] (Sampling)
#> Chain 1: Iteration: 40 / 50 [ 80%] (Sampling)
#> Chain 1: Iteration: 45 / 50 [ 90%] (Sampling)
#> Chain 1: Iteration: 50 / 50 [100%] (Sampling)
#> Chain 1:
#> Chain 1: Elapsed Time: 0.022 seconds (Warm-up)
#> Chain 1: 0.208 seconds (Sampling)
#> Chain 1: 0.23 seconds (Total)
#> Chain 1:
#> Warning: There were 11 divergent transitions after warmup. See
#> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
#> to find out why this is a problem and how to eliminate them.
#> Warning: There were 1 chains where the estimated Bayesian Fraction of Missing Information was low. See
#> https://mc-stan.org/misc/warnings.html#bfmi-low
#> Warning: Examine the pairs() plot to diagnose sampling problems
#> Warning: The largest R-hat is NA, indicating chains have not mixed.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#r-hat
#> Warning: Bulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#bulk-ess
#> Warning: Tail Effective Samples Size (ESS) is too low, indicating posterior variances and tail quantiles may be unreliable.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#tail-ess
#> Inference for the input samples (1 chains: each with iter = 25; warmup = 12):
#>
#> Q5 Q50 Q95 Mean SD Rhat Bulk_ESS Tail_ESS
#> x[1,1] -0.9 -0.3 0.0 -0.5 0.4 1.38 8 6
#> x[2,1] 0.3 1.1 1.9 1.1 0.6 2.12 4 13
#> x[1,2] -1.8 -1.0 -0.2 -1.0 0.6 1.60 4 6
#> x[2,2] 0.5 0.9 2.0 1.1 0.6 1.47 6 13
#> x[1,3] -2.5 -2.0 -0.7 -1.8 0.7 1.74 4 6
#> x[2,3] 1.4 1.8 2.4 1.9 0.4 2.07 11 13
#> x[1,4] -1.8 -0.7 0.6 -0.7 0.9 1.91 4 6
#> x[2,4] 1.5 2.3 2.5 2.1 0.4 2.07 4 13
#> x[1,5] -0.9 0.0 1.9 0.3 1.1 1.74 4 6
#> x[2,5] 1.7 2.4 2.7 2.3 0.4 1.45 9 13
#> x[1,6] -2.2 -1.9 0.0 -1.3 1.0 1.60 4 6
#> x[2,6] 0.4 1.5 1.9 1.3 0.6 1.58 5 13
#> x[1,7] -0.7 -0.4 2.0 0.3 1.2 1.91 4 6
#> x[2,7] -0.1 0.9 1.3 0.6 0.6 1.91 5 13
#> x[1,8] -0.8 -0.4 2.0 0.3 1.2 1.87 4 13
#> x[2,8] -0.6 0.3 0.5 0.0 0.5 1.87 4 13
#> x[1,9] 0.2 0.6 2.7 1.2 1.0 1.71 4 13
#> x[2,9] -0.8 0.4 1.3 0.3 0.9 1.91 4 13
#> x[1,10] 0.9 1.8 3.1 1.9 0.7 1.32 6 13
#> x[2,10] -1.8 -0.9 -0.1 -0.8 0.7 2.07 4 13
#> x[1,11] 0.8 1.7 3.0 1.8 0.7 1.49 6 13
#> x[2,11] -0.4 1.2 1.6 0.9 0.8 1.87 4 13
#> x[1,12] -0.7 0.7 1.2 0.4 0.7 0.91 10 13
#> x[2,12] -1.7 -1.0 0.0 -0.8 0.6 1.04 9 13
#> x[1,13] -0.4 0.7 1.7 0.5 0.7 1.49 13 13
#> x[2,13] -2.3 -1.8 -0.7 -1.5 0.6 1.03 13 13
#> x[1,14] -0.5 0.9 1.3 0.6 0.6 1.37 13 13
#> x[2,14] -1.9 -1.5 -0.5 -1.3 0.5 1.00 9 13
#> x[1,15] -0.7 1.2 1.6 1.0 1.1 1.32 5 13
#> x[2,15] -1.3 -0.8 0.1 -0.8 0.5 1.19 7 13
#> x[1,16] -1.0 1.2 1.4 0.8 1.3 1.87 13 13
#> x[2,16] -2.4 -1.9 -1.3 -1.8 0.4 1.71 9 13
#> x[1,17] -1.1 1.3 1.7 0.9 1.6 0.92 13 13
#> x[2,17] -3.5 -2.4 -2.2 -2.7 0.5 1.00 11 13
#> x[1,18] -2.0 0.5 1.3 0.2 1.8 0.93 13 13
#> x[2,18] -2.6 -1.5 -1.0 -1.7 0.6 0.98 10 13
#> x[1,19] -2.6 0.4 0.7 -0.2 1.8 2.07 4 13
#> x[2,19] -3.4 -2.7 -1.7 -2.5 0.7 0.98 12 13
#> x[1,20] -1.6 1.5 2.1 1.0 1.8 2.07 4 13
#> x[2,20] -2.5 -1.8 -0.7 -1.6 0.7 1.03 11 13
#> Z[1,1] -1.9 0.5 0.7 0.0 1.5 2.07 4 13
#> Z[2,1] -0.6 -0.1 0.3 -0.1 0.3 1.24 13 13
#> Z[3,1] -0.6 0.5 0.6 0.3 0.4 1.33 9 13
#> Z[4,1] -0.3 0.3 0.6 0.2 0.3 1.60 13 13
#> Z[1,2] 0.0 0.0 0.0 0.0 0.0 1.00 13 13
#> Z[2,2] -0.7 -0.4 0.0 -0.4 0.3 1.12 13 13
#> Z[3,2] 0.2 0.5 0.8 0.5 0.3 1.12 8 13
#> Z[4,2] -0.6 -0.4 0.3 -0.3 0.4 1.13 13 13
#> log_lik[1] -3.3 -1.2 -0.7 -1.5 1.4 1.33 5 13
#> log_lik[2] -3.1 -0.7 -0.5 -1.1 1.5 1.60 4 13
#> log_lik[3] -3.2 -0.6 -0.4 -1.1 1.5 1.71 4 13
#> log_lik[4] -3.4 -1.3 -0.5 -1.5 1.4 1.38 5 13
#> log_lik[5] -4.3 -1.9 -0.8 -2.0 1.4 1.71 4 13
#> log_lik[6] -3.7 -1.2 -0.5 -1.5 1.5 1.59 4 13
#> log_lik[7] -3.3 -1.0 -0.6 -1.3 1.4 1.30 13 13
#> log_lik[8] -4.1 -2.0 -0.7 -2.1 1.4 1.33 5 13
#> log_lik[9] -4.0 -1.0 -0.5 -1.6 1.6 1.87 4 13
#> log_lik[10] -4.0 -0.7 -0.4 -1.4 1.5 1.48 5 13
#> log_lik[11] -4.8 -1.2 -0.6 -1.7 1.6 0.92 10 13
#> log_lik[12] -3.5 -0.9 -0.5 -1.3 1.5 1.49 5 13
#> log_lik[13] -3.6 -0.9 -0.4 -1.4 1.5 1.91 4 13
#> log_lik[14] -3.5 -0.6 -0.5 -1.2 1.5 1.50 4 13
#> log_lik[15] -3.5 -0.7 -0.5 -1.3 1.5 1.48 5 13
#> log_lik[16] -3.2 -0.7 -0.5 -1.1 1.5 1.58 4 13
#> log_lik[17] -4.9 -2.1 -1.2 -2.4 1.4 1.49 4 13
#> log_lik[18] -3.4 -0.6 -0.5 -1.2 1.5 1.33 5 13
#> log_lik[19] -3.5 -1.0 -0.5 -1.4 1.5 1.47 13 13
#> log_lik[20] -4.2 -1.1 -0.6 -1.5 1.5 0.96 13 13
#> log_lik[21] -4.0 -1.6 -0.5 -1.9 1.4 1.18 8 13
#> log_lik[22] -3.3 -0.8 -0.5 -1.2 1.5 0.92 13 13
#> log_lik[23] -4.1 -1.6 -0.7 -1.8 1.4 1.47 13 13
#> log_lik[24] -4.7 -2.3 -1.1 -2.7 1.4 1.06 12 13
#> log_lik[25] -3.8 -0.9 -0.6 -1.5 1.5 2.07 4 13
#> log_lik[26] -3.1 -0.7 -0.5 -1.1 1.5 1.71 4 13
#> log_lik[27] -3.4 -0.8 -0.6 -1.3 1.5 1.19 13 13
#> log_lik[28] -3.3 -0.6 -0.4 -1.1 1.5 1.87 4 13
#> log_lik[29] -4.6 -1.4 -1.1 -2.1 1.4 2.07 4 13
#> log_lik[30] -4.5 -1.4 -1.2 -2.0 1.4 1.87 5 13
#> log_lik[31] -3.8 -0.8 -0.7 -1.3 1.5 1.58 10 13
#> log_lik[32] -4.0 -1.0 -0.6 -1.5 1.5 1.74 4 13
#> log_lik[33] -4.2 -1.9 -1.2 -2.2 1.3 1.47 8 13
#> log_lik[34] -4.7 -1.3 -0.6 -1.9 1.6 1.31 5 13
#> log_lik[35] -7.6 -2.0 -1.4 -3.0 2.5 1.25 5 13
#> log_lik[36] -3.6 -0.8 -0.4 -1.2 1.5 2.12 3 13
#> log_lik[37] -3.2 -1.0 -0.7 -1.3 1.4 0.98 12 13
#> log_lik[38] -4.5 -1.1 -0.7 -1.7 1.5 1.24 7 13
#> log_lik[39] -10.9 -4.6 -2.7 -5.7 3.8 1.71 9 13
#> log_lik[40] -3.1 -0.7 -0.5 -1.1 1.5 2.12 3 13
#> log_lik[41] -3.1 -0.6 -0.5 -1.1 1.5 1.04 13 13
#> log_lik[42] -4.1 -1.5 -0.7 -1.8 1.4 0.96 13 13
#> log_lik[43] -3.8 -1.2 -0.6 -1.6 1.4 1.03 13 13
#> log_lik[44] -3.9 -1.4 -0.7 -1.7 1.4 1.49 13 13
#> log_lik[45] -3.3 -0.9 -0.5 -1.3 1.5 1.12 7 13
#> log_lik[46] -3.1 -0.8 -0.4 -1.1 1.5 1.19 6 13
#> log_lik[47] -3.1 -0.7 -0.4 -1.0 1.5 1.60 4 13
#> log_lik[48] -3.2 -0.8 -0.4 -1.1 1.5 1.32 6 13
#> log_lik[49] -3.8 -1.3 -1.1 -1.8 1.3 0.96 13 13
#> log_lik[50] -3.9 -1.2 -0.7 -1.7 1.4 1.19 7 13
#> log_lik[51] -4.8 -1.9 -1.2 -2.3 1.3 0.98 13 13
#> log_lik[52] -3.5 -0.9 -0.5 -1.4 1.5 1.14 7 13
#> log_lik[53] -3.1 -0.6 -0.4 -1.0 1.5 1.50 5 13
#> log_lik[54] -3.1 -0.7 -0.5 -1.1 1.5 1.25 13 13
#> log_lik[55] -3.1 -0.7 -0.6 -1.1 1.5 1.19 8 13
#> log_lik[56] -3.1 -0.7 -0.5 -1.2 1.5 0.95 13 13
#> log_lik[57] -3.2 -0.7 -0.4 -1.2 1.5 2.12 4 13
#> log_lik[58] -3.5 -0.9 -0.5 -1.3 1.5 1.87 4 13
#> log_lik[59] -3.2 -0.8 -0.6 -1.2 1.5 1.19 9 13
#> log_lik[60] -3.1 -0.7 -0.5 -1.1 1.5 1.87 4 13
#> log_lik[61] -3.2 -0.7 -0.5 -1.2 1.5 2.07 4 13
#> log_lik[62] -3.1 -0.7 -0.5 -1.1 1.5 1.48 5 13
#> log_lik[63] -3.5 -0.7 -0.5 -1.3 1.5 1.49 5 13
#> log_lik[64] -3.1 -0.7 -0.4 -1.1 1.5 1.30 6 13
#> log_lik[65] -3.4 -1.2 -0.7 -1.5 1.4 1.87 4 13
#> log_lik[66] -3.1 -0.7 -0.5 -1.1 1.5 1.71 4 13
#> log_lik[67] -3.3 -1.0 -0.4 -1.3 1.5 2.12 4 13
#> log_lik[68] -3.3 -0.6 -0.5 -1.1 1.5 1.48 5 13
#> log_lik[69] -3.5 -1.4 -0.8 -1.7 1.3 1.30 5 13
#> log_lik[70] -3.4 -1.3 -0.7 -1.5 1.4 1.05 13 13
#> log_lik[71] -3.4 -1.1 -0.6 -1.5 1.4 1.27 7 13
#> log_lik[72] -3.1 -0.7 -0.5 -1.1 1.5 1.60 5 13
#> log_lik[73] -3.8 -1.6 -1.1 -1.9 1.3 1.71 4 13
#> log_lik[74] -3.4 -0.6 -0.5 -1.2 1.5 1.48 5 13
#> log_lik[75] -3.2 -0.7 -0.4 -1.2 1.5 1.16 6 13
#> log_lik[76] -3.9 -0.6 -0.5 -1.2 1.5 1.60 4 13
#> log_lik[77] -3.2 -0.7 -0.5 -1.2 1.5 1.87 4 13
#> log_lik[78] -3.6 -0.7 -0.4 -1.2 1.5 1.31 5 13
#> log_lik[79] -4.8 -1.2 -0.5 -1.7 1.6 1.19 6 13
#> log_lik[80] -3.1 -0.7 -0.4 -1.1 1.5 1.48 5 13
#> xstar[1,1] -2.2 1.7 2.5 0.9 2.0 1.07 13 13
#> xstar[2,1] -3.2 -1.5 0.3 -1.5 1.2 1.22 7 13
#> sigma[1] 0.6 0.7 68.8 13.8 47.0 1.40 5 6
#> lp__ -277.2 -79.7 -59.9 -113.1 119.6 2.12 3 13
#>
#> For each parameter, Bulk_ESS and Tail_ESS are crude measures of
#> effective sample size for bulk and tail quantities respectively (an ESS > 100
#> per chain is considered good), and Rhat is the potential scale reduction
#> factor on rank normalized split chains (at convergence, Rhat <= 1.05).
fitted <- dfa_fitted(m)
# }